A Framework for the Generation of Guaranteed Stable Small-Signal Bias-Dependent Behavioral Models

Abstract

We present a numerical scheme for the identification of compact surrogate models of analog circuit blocks. The basic assumption is small signal operation, so that a local linearization can be applied around a given bias point, resulting in a bias-dependent linear state-space behavioral macromodel. The main novel contribution of this work is the ability to embed in the identification process a suitable set of constraints, that are able to guarantee the uniform stability of the model for any bias value within a prescribed design range.